2023
DOI: 10.1007/978-3-031-21438-7_69
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Computational Assessment Model for Blind Medical Image Watermarking with Deep Learning

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Cited by 1 publication
(2 citation statements)
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“…Studies have consistently shown that benchmarks can reveal sophisticated insights into how biases manifest in different contexts, offering a more granular understanding of model behavior [19], [59]- [63]. There is a growing consensus that benchmarks must evolve to capture the multidimensional nature of fairness, incorporating diverse perspectives and scenarios [64]- [67]. Research has also highlighted the importance of transparency and interpretability in benchmark results, emphasizing that stakeholders should understand the basis of evaluations to trust and act on them [68]- [71].…”
Section: Evaluating Fairness and Benchmarks In Ai Modelsmentioning
confidence: 99%
See 1 more Smart Citation
“…Studies have consistently shown that benchmarks can reveal sophisticated insights into how biases manifest in different contexts, offering a more granular understanding of model behavior [19], [59]- [63]. There is a growing consensus that benchmarks must evolve to capture the multidimensional nature of fairness, incorporating diverse perspectives and scenarios [64]- [67]. Research has also highlighted the importance of transparency and interpretability in benchmark results, emphasizing that stakeholders should understand the basis of evaluations to trust and act on them [68]- [71].…”
Section: Evaluating Fairness and Benchmarks In Ai Modelsmentioning
confidence: 99%
“…The ethical dimension of AI development has gained increased attention, with calls for more transparent and accountable modeling practices that prioritize ethical considerations [73], [74]. Moreover, there is a growing acknowledgment of the role of diverse stakeholder engagement in guiding the ethical development of AI, emphasizing the need for multidisciplinary collaboration in addressing the challenges posed by LLM bias [3], [67], [75], [76].…”
Section: Ethical Considerations and Societal Impacts Of Llm Biasmentioning
confidence: 99%